TY - CHAP

T1 - QALD-9-ES: A Spanish Dataset for Question Answering Systems

AU - Soruco, Javier

AU - Collarana, Diego

AU - Both, Andreas

AU - Usbeck, Ricardo

N1 - Conference code: 19

PY - 2023/9/11

Y1 - 2023/9/11

N2 - Knowledge Graph Question Answering (KGQA) systems enable access to semantic information for any user who can compose a question in natural language. KGQA systems are now a core component of many industrial applications, including chatbots and conversational search applications. Although distinct worldwide cultures speak different languages, the number of languages covered by KGQA systems and its resources is mainly limited to English. To implement KGQA systems worldwide, we need to expand the current KGQA resources to languages other than English. Taking into account the recent popularity that LargeScale Language Models are receiving, we believe that providing quality resources is key to the development of future pipelines. One of these resources is the datasets used to train and test KGQA systems. Among the few multilingual KGQA datasets available, only one covers Spanish, i.e., QALD-9. We reviewed the Spanish translations in the QALD-9 dataset and confirmed several issues that may affect the KGQA system’s quality. Taking this into account, we created new Spanish translations for this dataset and reviewed them manually with the help of native speakers. This dataset provides newly created, high-quality translations for QALD-9; we call this extension QALD-9-ES. We merged these translations into the QALD-9-plus dataset, which provides trustworthy native translations for QALD-9 in nine languages, intending to create one complete source of high-quality translations. We compared the new translations with the QALD-9 original ones using Languageagnostic quantitative text analysis measures and found improvements in the results of the new translations. Finally, we compared both translations using the GERBIL QA benchmark framework using a KGQA system that supports Spanish. Although the question-answering scores only improved slightly, we believe that improving the quality of the existing translations will result in better KGQA systems and therefore increase the applicability of KGQA w.r.t. the Spanish language domain.

AB - Knowledge Graph Question Answering (KGQA) systems enable access to semantic information for any user who can compose a question in natural language. KGQA systems are now a core component of many industrial applications, including chatbots and conversational search applications. Although distinct worldwide cultures speak different languages, the number of languages covered by KGQA systems and its resources is mainly limited to English. To implement KGQA systems worldwide, we need to expand the current KGQA resources to languages other than English. Taking into account the recent popularity that LargeScale Language Models are receiving, we believe that providing quality resources is key to the development of future pipelines. One of these resources is the datasets used to train and test KGQA systems. Among the few multilingual KGQA datasets available, only one covers Spanish, i.e., QALD-9. We reviewed the Spanish translations in the QALD-9 dataset and confirmed several issues that may affect the KGQA system’s quality. Taking this into account, we created new Spanish translations for this dataset and reviewed them manually with the help of native speakers. This dataset provides newly created, high-quality translations for QALD-9; we call this extension QALD-9-ES. We merged these translations into the QALD-9-plus dataset, which provides trustworthy native translations for QALD-9 in nine languages, intending to create one complete source of high-quality translations. We compared the new translations with the QALD-9 original ones using Languageagnostic quantitative text analysis measures and found improvements in the results of the new translations. Finally, we compared both translations using the GERBIL QA benchmark framework using a KGQA system that supports Spanish. Although the question-answering scores only improved slightly, we believe that improving the quality of the existing translations will result in better KGQA systems and therefore increase the applicability of KGQA w.r.t. the Spanish language domain.

KW - Business informatics

KW - Knowledge Graphs

KW - Informatics

KW - Question Answering

KW - Dataset

UR - https://www.iospress.com/catalog/books/knowledge-graphs-semantics-machine-learning-and-languages

UR - https://www.mendeley.com/catalogue/863f1283-9035-351a-bc12-b1ae046d0649/

U2 - 10.3233/SSW230004

DO - 10.3233/SSW230004

M3 - Article in conference proceedings

SN - 978-1-64368-424-6

T3 - Studies on the Semantic Web

SP - 38

EP - 52

BT - Knowledge Graphs: Semantics, Machine Learning, and Languages

A2 - Acosta, Maribel

A2 - Peroni, Silvio

A2 - Vahdati, Sahar

A2 - Gentile, Anna Lisa

A2 - Pellegrini, Tassilo

A2 - Kalo, Jan-Christoph

PB - IOS Press BV

CY - Amsterdam

T2 - 19th International Conference on Semantic Systems

Y2 - 20 September 2023 through 22 September 2023

ER -